Programming languages for matrix computations
|Starts:||15:00 10 May 2019|
|Ends:||16:00 10 May 2019|
|What is it:||Seminar|
|Organiser:||Department of Mathematics|
|Who is it for:||University staff, External researchers, Adults, Alumni, Current University students|
|Speaker:||Professor Paolo Bientinesi|
Join us for this research seminar, part of the Numerical analysis and scientific computing seminar series.
Matrix computations appear in virtually every domain of science and engineering, and due to the seemingly unstoppable growth of data science, are now as widespread as ever. Such a massive demand triggered two distinct developments. On the one hand, the numerical linear algebra community has put tremendous effort in the identification, analysis and optimization of a reasonably small set of simple operations---such as those included in the BLAS and LAPACK libraries---that serve as building blocks for most users' target computations. On the other hand, the computer science community delivered several high-level programming languages---such as Matlab, Julia, R---that make it possible to code matrix computations at the same level of abstraction at which experts reason about them. Under the cover, all such languages face the problem of expressing a target matrix computation in terms of said building blocks; we refer to this problem as the "Linear Algebra Mapping Problem" (LAMP). In this talk we define the problem, present the challenges it poses, and carefully survey how it is (currently) solved by the state-of-the-art languages. Finally, we introduce Linnea, our compiler for matrix computations.
Professor Paolo Bientinesi
Organisation: Department of Computing Science, Umeå University
Biography: See Professor Paolo Bientinesi's profile:
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